Generally Electronic commerce or E-commerce applications such as EBay and Amazon use reputation reporting system for trust evaluation where they gather overall feedback ratings from the sellers to compute the reputation score for a seller. The main issue raised with the reputation conduct system is "all good reputation" problem where most of feedback ratings are positive leading to high reputation scores for all sellers. In this case it is difficult for buyers to select the best or accurate seller that he/she can buy from. So in order to overcome this issue we propose an approach called the Comm Trust which evaluates the multidimensional trust for seller by analyzing buyer's opinions on free text feedback comments. The main idea behind reputation analyzer is an algorithm CommTrust algorithm which is a topic modeling technique proposed for mining the online feedback comments by grouping aspect expressions into dimensions and compute dimension ratings for a seller.

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Nlp Topic Modeling social Network Recommendation System Similarity Graph etc.